(2024) 22:300
Lu et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03528-3

BMC Medicine

RESEARCH ARTICLE

Open Access

Early and long‑term responses of intestinal
microbiota and metabolites to 131I treatment
in differentiated thyroid cancer patients
Ganghua Lu1†, Dingwei Gao1†, Yixian Liu2†, Xiaqing Yu1*, Wen Jiang1,3* and Zhongwei Lv1,4*Abstract
Background Multiple high doses of 131I therapy in patients with differentiated thyroid cancer (DTC) might disrupt
the balance of gut microbiota and metabolites. This study aimed to investigate the alterations of intestinal bacteria
and metabolism over two courses of 131I therapy, explore the interactions, and construct diagnostic models reflecting
enteric microecology based on 131I therapy.
Methods A total of 81 patients were recruited for the first 131I therapy (131I-1st), among whom 16 received a second
course (131I-2nd) after half a year. Fecal samples were collected 1 day before (Pre-131I-1st/2nd) and 3 days after (Post131
I-1st/2nd) 131I therapy for microbiome (16S rRNA gene sequencing) and metabolomic (LC–MS/MS) analyses.
Results A total of six microbial genera and 11 fecal metabolites enriched in three pathways were identified to show
significant differences between Pre-131I-1st and other groups throughout the two courses of 131I treatment. In
the Post-131I-1st group, the beneficial bacteria Bifidobacterium, Lachnoclostridium, uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004 were abundant and the radiation-sensitive pathways of linoleic acid (LA),arachidonic acid, and tryptophan metabolism were inhibited compared with the Pre-131I-1st group. Comparedwith the Pre-131I-1st group, the Pre-131I-2nd group exhibited a reduced diversity of flora and differentially expressed
metabolites, with a low abundance of beneficial bacteria and dysregulated radiation-sensitive pathways. However,less significant differences in microbiota and metabolites were found between the Pre/Post-131I-2nd groups compared with those between the Pre/Post-131I-1st groups. A complex co-occurrence was observed between 6 generaand 11 metabolites, with Lachnoclostridium, Lachnospiraceae_UCG004, Escherichia-Shigella, and LA-related metabolites
contributing the most. Furthermore, combined diagnostic models of charactered bacteria and metabolites answered
well in the early, long-term, and dose-dependent responses for 131I therapy.
Conclusions Different stages of 131I therapy exert various effects on gut microecology, which play an essential role
in regulating radiotoxicity and predicting the therapeutic response.
Keywords 131I therapy, Gut microbiota, Fecal metabolites, Linoleic acid†

Ganghua Lu, Dingwei Gao, and Yixian Liu contributed equally to this work.

*Correspondence:
Xiaqing Yu
yuxiaqing93@163.com
Wen Jiang
2011704@tongji.edu.cn
Zhongwei Lv
Lvzwjs2020@163.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Lu et al. BMC Medicine(2024) 22:300

BackgroundAs the most common endocrine malignancy, thyroid
cancer (TC) has an increasing annual global incidence,with differentiated thyroid cancer (DTC) accounting for
approximately 90% of the cases [1]. Most patients with
DTC exhibit positive outcomes after standard treatments, including surgery, 131I, and levothyroxine replacement for suppression of thyroid hormones. However,some patients require multiple 131I therapies with higher
doses due to the residual thyroid tissue and local or distant metastatic mass, which increases the chances ofadverse reactions in the hematopoietic, gastrointestinal,and even marrow systems [2, 3]. Nonetheless, few noninvasive tests are available to investigate the early and longterm responses of DTC patients under 131I therapy.
Since 131I is administered orally, it accumulates in thegastrointestinal tract and may affect gut microbes [4].
Until now, many studies have reported that ionizing radiation (IR) affects the structure, function, and species of
the gut microbiota and its metabolites by triggering dysbiosis, further exacerbating damage induced by IR [5–8].
On the contrary, beneficial bacteria and favorable metabolites could rebuild the IR-injured gut microbial structure
via anti-inflammatory and anti-oxidant mechanisms [6, 9,10]. In addition, previous studies also revealed a significant difference in gut microbes and metabolites between
DTC patients and health controls, with the imbalanced
composition of microbiota and homeostasis of lipid
metabolism possibly accelerating the progression of thyroid cancer [11, 12]. Interestingly, profiles of gut microbiota could also predict the treatment effect of 131I therapy
in DTC patients [4]. All these findings indicated that the
gut microbiome and its related metabolites were closely
related to DTC and 131I therapy, highlighting its importance in the prevention of radiotoxicity and risk factors of
DTC progression.
Nevertheless, the functional alterations in entericmicrobiota and their radiation-associated metabolites atdifferent stages of 131I treatment in DTC remain unclear.
Hence, gut 16S-rDNA sequencing and metabolome analyses were performed in a cohort of patients with DTC
over two courses to validate 131I-related changes in the
microbial and metabolic signatures of DTC patients.
Methods
Study subjects and design

The study enrolled 81 patients with DTC undertaking
the first 131I therapy (131I-1st) and 16 follow-ups among
them after half a year undertaking the second 131I therapy(131I-2nd) from the Department of Nuclear Medicine,
Shanghai Tenth People’s Hospital, between March 2021
and March 2022 (Table 1). The diagnosis of DTC was

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Table 1 Baseline characteristics of patients in 131I-1st and 131I2nd groups
Variables131

I-1st (n = 81)

131

I-2nd (n = 16) P value

Gender, male (%)

23 (28)7 (46)

0.246

Age, years

47 ± 1549 ± 11

0.647
0.488

BMI, kg/m2

25.30 ± 4.25

26.09 ± 3.45

131

100 (100–120)

135 (100–195)

0.047*

Clinical stage, II-IV (%)

9 (11)

8 (50)

0.001*

I, mCiThyroid function
TSH, mIU/L

106.00 ± 33.73

104.87 ± 36.93

0.904

Tg, ng/mL

38.06 ± 104.32

75.40 ± 146.88

0.240

20 (26)

2 (12)

0.344

TgAb, ≥ 100.00 IU/mL(%)
Routine blood
WBC, ­109/L

5.80 (4.92–6.92)

6.53 (5.52–8.35)

0.143

RBC, ­1012/L

4.70 ± 0.44

4.62 ± 0.35

0.502

PLT, ­109/L

246 ± 65226 ± 75

0.292

Hb, g/L

139 ± 18142 ± 130.493

Gran, %

59.2 (53.8–69.2)

63.9 (58.6–71.0)

0.081

Lymph, %31.8 ± 8.6

26.6 ± 8.8

0.035*Inflammation
TNFα, pg/mL

6.48 (5.26–8.10)

5.78 (5.06–6.48)

0.181

IL-1, ≥ 5.00 pg/mL (%)

16 (21)3 (19)

1.000

IL-2, ≥ 200.00 u/mL (%)

20 (26)7 (44)

0.224

IL-6, ≥ 2.00 pg/mL (%)

42 (55)

10 (62)

0.594

CRP, ≥ 3.13 mg/L (%)

11 (14)

1 (6)

0.684

Liver function
TBI, μmol/L

13.8 ± 6.0

13.5 ± 5.1

0.887

DBI, μmol/L

4.2 ± 1.5

4.2 ± 1.5

0.988

TCh, mmol/L

6.41 (5.75–7.60)

6.68 (5.60–7.70)

0.612

TG, mmol/L

1.92 (1.26–2.68)

1.89 (1.10–3.20)

0.952

HDL, mmol/L

1.8 ± 0.4

1.8 ± 0.4

0.874

LDL, mmol/L

3.9 (3.2–4.7)

3.7 (3.1–4.9)

0.929

Data were presented as numbers of patients (n, %), mean ± standard deviations
or median (interquartile range).
*Comparison between 131I-1st and 131I-2nd groups.
BMI body mass index, CRP C-reactive protein, DBI direct bilirubin, DTC
differentiated thyroid cancer, Gran neutrophil, Hb hemoglobin, HDL high density
lipoprotein, IL-1 interleukin 1, IL-2 interleukin 2 receptor, IL-6 interleukin 6, LDL
low density lipoprotein, Lymph lymphocyte, PLT platelet, RBC red blood cell,
TBI total bilirubin, TCh total cholesterol, TG triglyceride, Tg thyroglobulin, TgAb
thyroglobulin antibodies, TNFα tumor necrosis factor alpha, TSH thyrotropin,
WBC white blood cell. 131I-1st group, patients with DTC undertaking the first 131I
therapy; 131I-2nd group, patients with DTC undertaking the second 131I therapy.

based on the following criteria: (i) clinically diagnosed
with DTC within 6 months [13], (ii) age between 18 and75 years, female (nonpregnant) or male without historyof other malignancies, and (iii) receiving 131I treatment.
The following patients were also excluded: (i) patients
confirmed not to have thyroid cancer on postoperative
pathology, (ii) subjects suffering from a gastrointestinal
disease or any other severe physical or mental disease,Lu et al. BMC Medicine(2024) 22:300

and (iii) subjects with a history of probiotics, orally
ingested antibiotics, or any other similar drug within
2 months before fecal sampling. Blood routine examination, thyroid function, and biochemical and inflammatory indicators in the blood samples were measured(Table 1). The study was carried out following the Declaration of Helsinki principles and approved by the Ethics
Committee of the Shanghai Tenth People’s Hospital (No.
SHSY-IEC-5.0/22K13/P01). Informed, written consent
was obtained from all patients.
Collection of fecal samples

Fecal samples were collected in the morning after anovernight fast (> 8 h) 1 day before (Pre-131I-1st/2nd) and3 days after (Post-131I-1st/2nd) 131I treatment. Fecal samples were snap-frozen with liquid nitrogen, collected, andstored at − 80 °C (Haier, DW-86L626, China) [14].
Microbiomic analysis

Microbial DNA was extracted from fecal samples using a
QIAamp Fast DNA stool Mini Kit (Qiagen, Cat# 51,604)
and amplified with barcoded bacterial primers targeting the 16S rRNA variable region 3–4 (V3–V4): forward
primer338F:5′-ACT​CCT​ACG​GGA​GGC​AGC​A-3′
and reverse primer 806R:5′-GGA​CTA​CHVGGG​T WT​CTAAT-3′ [15]. Illumina NovaSeq6000 was used forthe construction of sequencing libraries and paired-end
sequencing of samples. Paired-end reads were mergedusing FLASH v1.2.7, and tags with more than six mismatches were removed [16]. Using Trimmomatic, tagswith a quality score of 20 within a sliding window of
50 bps were determined, and those shorter than 350
bps were removed [17]. A total of 172 chimeras were
removed from the denoised sequences and they wereclustered into operational taxonomic units (OTUs) with
a 97% similarity using the USEARCH (version 10.2) program. All OTUs were assigned a taxonomy by searching
the Silva databases (Release128) using QIIME.
Untargeted metabolomic analysis

Fifty milligrams of fecal sample was weighed into an EP
tube, and 1000 μL of extract solution (methanol: acetonitrile: water = 2:2:1, with isotopically labeled internal standard mixture) was added. The samples werethen homogenized at 35 Hz for 4 min and sonicated for
5 min in an ice-water bath. Homogenization and sonication cycles were repeated for three times. The samples
were then incubated for 1 h at − 40 °C and centrifuged at
12,000 rpm for 15 min at 4 °C. The resulting supernatant
was transferred into a fresh glass vial for further analysis. A quality control sample was prepared by mixing anequal aliquot of the supernatant from all samples.

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LC–MS/MS analysis

LC–MS/MS analyses were performed using an UHPLC
system (Vanquish, Thermo Fisher Scientific) with a UPLC
BEH Amide column (2.1 mm × 100 mm, 1.7 μm) coupled to Q Exactive HFX mass spectrometer (Orbitrap
MS, Thermo). The mobile phase consisted of 25 mmol/Lammonium acetate and 25 ammonia hydroxides in water(pH = 9.75) (A) and acetonitrile (B). The auto-sampler
temperature was 4 ℃, and the injection volume was 3 μL.
The QE HFX mass spectrometer was used for its ability
to acquire MS/MS spectra on information-dependentacquisition (IDA) mode in the control of the acquisition
software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluates the full scan MS
spectrum. The ESI source conditions were set as follows:
sheath gas flow rate as 30 Arb, Aux gas flow rate as 25
Arb, capillary temperature 350 ℃, full MS resolution as
60,000, MS/MS resolution as 7500, collision energy as
10/30/60 in NCE mode, spray Voltage as 3.6 kV (positive)or − 3.2 kV (negative), respectively.
Statistical analysis

Data are presented as the mean ± standard deviation or
median (interquartile range). To assess significance, themean values of independent groups were compared using
Student’s t test and Wilcoxon rank-sum test; categoricalvariables were assessed by the chi-square test as follows:*, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001. False
discovery rate (FDR) was used to adjust p values in differential expressed bacteria and metabolite analyses. Statistical significance was set at p < 0.05.

Results
Clinical characteristics of the participants

The basic information of the 81 DTC patients receiving
131
I therapy for the first time (131I-1st), among whom
16 received a second course (131I-2nd), are displayed inTable 1. Compared with the 131I-1st group, the 131I-2ndgroup showed a higher clinical stage (II/III/IV) and doseof 131I treatment, but lower percentage of lymphocytes.
In contrast, gender, age, body mass index (BMI), thyroid
function, inflammation, and liver function showed nosignificant difference between the groups.
Features of gut bacterial community compositionand structure

Furthermore, microbiome analyses were performed on
fecal samples from DTC patients before and after 131I
therapy (Fig. 1). The results were processed for quality filtering, and chimeric sequences were removed,
yielding an average of 58,152 sequences (derived from67,859 raw reads) for abundance and diversity analyses.

Lu et al. BMC Medicine(2024) 22:300

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Fig. 1 Overview of the workflow for the microbiomic and metabolomic strategy used in this study

The rarefaction curve revealed great species richness
and evenness (Fig. 2A) and the rank abundance curve(Fig. 2B) confirmed the thoroughness of the sample collection. The α diversity of Ace, Chao1, PD_whole_tree(Fig. 2C-E), Simpson, and Shannon (Additional file 1:
Fig.S1A, B) indices were significantly decreased in the
Post-131I-1st group compared to the Pre-131I-1st groupand in the Post-131I-2nd group compared to the Pre131
I-2nd group. The Pre-131I-2nd group demonstrated asmall reduction compared to the Pre-131I-1st group. In
addition, principal coordinate analysis (PCoA) based on
binary and unweighted distances (Fig. 2F, G) revealed significant differences between the Pre-131I-1st/2nd groups
and Post-131I-1st/2nd groups.
Moreover, remarkable differences were observed inall groups from the Phylum to Genus levels (Fig. 2H,I). At the Phylum level, the relative abundance of Firmicutes was elevated in the Post-131I-1st/2nd groups
compared to the Pre-131I-1st/2nd groups, which wasinconsistent with Proteobacteria (Fig. 2H) and the ratioof Firmicutes/Bacteroidetes (F/B, Additional file 1: Fig.
S1C). At the Family level, Enterobacteriaceae decreasedin the Post-131I-1st group compared with Pre-131I-1stgroup, whereas Lachnospiraceae and Bifidobacteriaceae
were enriched in the Post-131I-1st group (Fig. 2I). However, the differences between the Pre-and Post-131I-2ndgroups were not considerable. A broad overview of thetaxonomic data at the Genus level of all samples is shownin Fig. 2J.
Alterations in microbial genus compositions at different
stages of DTC with 131I therapy

To investigate the difference in bacterial composition
among the 4 groups, linear discriminate analysis effectsize (LEfSe) analysis (Fig. 3A–C) and the Wilcoxon ranksum test (Additional file 2: Fig.S2A-C) were conducted.

According to the above results, two altered patterns wereidentified on the genus level. The first pattern showed anincreased abundance, while the second pattern presented
a decreased abundance in the Post-131I-1st/2nd groups
compared to the Pre-131I-1st/2nd groups. The first pattern included four genera, including Bifidobacterium,
Lachnoclostridium,
uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004 (Fig. 3D). The
second pattern included two genera, namely Akkermansia and Escherichia-Shigella (Fig. 3D). Intriguingly, uncultured_bacterium_f_Lachnospiraceae in the first patternshowed a lower abundance in the Pre-131I-2nd groupcompared to the Pre-131I-1st group, displaying an opposite trend in pattern2 (Fig. 3B). Similar with the results
at the Family level, the differences between the Pre- andPost-131I-2nd groups were not obvious (Fig. 2H; Fig. 3D).
Additionally, the Post-131I-1st/2nd groups were integrated and divided into the 131I-high dose (for metastatic
diseases, n = 24, ≥ 150 mCi) and 131I-low dose (for thyroid
remnants or adjuvant therapy, n = 73, < 150 mCi) groups[18, 19]. Surprisingly, the abundance of Lachnospiraceae_
UCG004 was significantly improved in the high-dosegroup (Fig. 3E), showing reversed results to EscherichiaShigella (Fig. 3F) and Akkermansia (Additional file 2: Fig.
S2D).
Linear discriminate analysis (LDA) effect size (LEfSe)
revealing differential microbiota on the genus level
between Post- and Pre-131I-1st (A), Pre-131I-2nd andPre-131I-1st (B), Post- and Pre-131I-2nd (C), charactered genera marked with red. D Taxonomic analysis
showed six microbial genera were changed among fourgroups. Abundance of g_Lachnospiraceae_UCG004 (E)and g_Escherichia-Shigella (F) between low (< 150 mCi)and high (≥ 150 mCi) dose of 131I therapy. * p < 0.05, **p < 0.01, *** p < 0.001, **** p < 0.0001. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with

Lu et al. BMC Medicine(2024) 22:300

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Fig. 2 Features of gut bacterial community composition and structure. α diversity analysis in rarefaction curve (A), rank abundance curve (B),Ace (C), Chao1 (D), and PD_whole_tree (E) indices among four groups. β diversity analysis on principal coordinates analysis (PCoA) for binary (F)and unweighted distances (G) among four groups. Abundance distribution on Phylum (H) and Family (I) level among four groups. J Annotatedabundance heatmap of gut microbial features of the top 20 genera based on Ward hierarchical clustering. * p < 0.05, ** p < 0.01, *** p < 0.001,**** p < 0.0001. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patientswith DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC
after the second 131I therapy

DTC before the first 131I therapy; Post-131I-1st group,
patients with DTC after the first 131I therapy; Pre-131I2nd group, patients with DTC before the second 131I
therapy; Post-131I-2nd group, patients with DTC after
the second 131I therapy.

Fecal metabolomic profiles based on 131I treatment
progress

Next, the metabolome of the enteric flora was evaluatedin DTC patients to reveal the alterations in metabolites
during continued 131I therapy (Fig. 1). The score plots of

Lu et al. BMC Medicine(2024) 22:300

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Fig. 3 Alterations in microbial genus compositions at different stages of DTC with 131I therapy

principal components analysis (PCA) and partial leastsquare regressions-discriminant analysis (PLS-DA)
clearly separated the Pre-131I-1st/2nd groups from Post131
I-1st/2nd groups (Fig. 4A, B). These findings revealed
an obvious metabolite variability across different stagesof 131I treatment. According to the results of pairwise
contrasts by volcano plots, a greater number of downregulated differential metabolites were observed in the
Post-131I-1st group compared to the Pre-131I-1st group(Fig. 4C). In addition, fewer metabolites were upregulated in the Pre-131I-2nd group compared to the Pre131
I-1st group (Fig. 4D); however, no distinct difference
was found between the Pre- and Post-131I-2nd groups(Fig. 4E). Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis was conducted forall differential metabolites, further exploring their biological function (Fig. 4F–H). Notably, the two essential

metabolic pathways involved in LA metabolism and arachidonic acid (ARA) metabolism were strongly downregulated in the Post-131I-1st/2nd groups compared to thePre-131I-1st/2nd groups (Fig. 4F, H). The pathways of LA
and tryptophan metabolism were downregulated in the
Pre-131I-2nd group compared to the Pre-131I-1st group(Fig. 4G).
To investigate the influence of the differential metabolites in these three metabolic pathways (radiationsensitive metabolic pathways), reaction pathways
were manually constructed using the KEGG pathway
reference map (Fig. 5). The results revealed that fivemetabolites (alpha-dimorphecolic acid, 13-OxoODE,
13-L-Hydroperoxylinoleic acid, PC(22:2(13Z,16Z)/14:0),
and 13S-hydroxyoctadecadienoic acid) derived fromthe LA metabolism pathway, four metabolites (5,6DHET, 20-Carboxy-leukotriene B4, 6-KetoprostaglandinLu et al. BMC Medicine(2024) 22:300

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Fig. 4 Fecal metabolomic profiles in accordance with 131I treatment progress. Principle components analysis (PCA) (A) and partial least square
regressions-discriminant analysis (PLS-DA) (B) results among four groups. Volcano plot of differential metabolites (fold change ≤ 0.5 or > 2)
between Post- and Pre-131I-1st (C), Pre-131I-2nd and Pre-131I-1st (D), Post-and Pre-131I-2nd (E). Differential abundance scores (DAS) of KEGG
pathways between Post- and Pre-131I-1st (F), Pre-131I-2nd and Pre-131I-1st (G), Post- and Pre-131I-2nd (H), radiation-sensitive metabolic pathways
marked red. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patientswith DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC
after the second 131I therapy

E1, delta-12-Prostaglandin J2) derived from the ARA
metabolism pathway, and two metabolites (indoleacetic
acid, formylanthranilic acid) derived from the tryptophan metabolism pathway were consistent with theirpathways’ regulation by 131I therapy. Meanwhile, a close
metabolic association was found between the LA and
ARA metabolism pathways (Fig. 5). Our previous study
and other research have reported that the administration

Lu et al. BMC Medicine(2024) 22:300

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Fig. 5 Changes of gut metabolites across the 131I therapy summarized in KEGG pathways. 131I therapy-related changes of gut metabolites
summarized in radiation-sensitive metabolic pathways among four groups according to KEGG pathway reference map. * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group,
patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patientswith DTC after the second 131I therapy

of metabolites in ARA and tryptophan metabolism pathways could exert an anti-radiotoxicity effect [6, 9, 20,21]. Additionally, anti-inflammatory and antioxidative
effects have also been linked to intestinal LA metabolism
pathways [22, 23]. Our study also preliminarily revealed
that the supplementation of LA in mice undergoing 131I
therapy could provide some protection against radiation
toxicity (Additional file 3: Fig.S3A-S3F; Additional file 4:
Supplemental Methods). Therefore, metabolites in theseradiation-sensitive metabolic pathways might play a crucial role in radiation protection.
Disrupted gut microbiota interactions with fecal
metabolism and clinical factors associated with DTC
patients under 131I therapy

An integrated network analysis was performed according to the microbial and metabolic data to identify
the possible mechanisms between differential intestinal flora and metabolites in 131I therapy (Fig. 6A, B;
Additional file 5: Fig.S4A-C). Consequently, an elaborate co-existence pattern and a robust connection

were found between 131I therapy progress-related
microbes and metabolites from the three radiationsensitive metabolic pathways (Fig. 6A). Meanwhile,
Lachnoclostridium, Lachnospiraceae_UCG004, andEscherichia-Shigella counts were closely correlatedwith fecal metabolites in the LA metabolism pathways (Fig. 6B), which were recognized as main contributors in this integrated network, suggesting theircrucial roles in the different phases of 131I therapy.
Interestingly, Bifidobacterium, Lachnoclostridium,
uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004 were negatively associated withthe pathways of LA, ARA, and tryptophan metabolism,with a converse trend in Akkermansia and EscherichiaShigella (Fig. 6A, B). Furthermore, the α diversity of
all the indices was strongly negatively correlated with
Escherichia-Shigella, but positively correlated with
F/B, uncultured_bacterium_f_Lachnospiraceae, Lachnospiraceae_UCG004, and metabolites in 3 pathways(Fig. 6C). Moreover, the redundancy analysis was performed to verify the impact of metabolites (Fig. 6D)

Lu et al. BMC Medicine(2024) 22:300

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Fig. 6 Disrupted gut microbiota interactions with fecal metabolism and clinical factors associated with DTC patients under 131I therapy. A
Integrated network of 131I therapy -related microbial genera and fecal metabolites. Networks were constructed using NAMAP with Spearman’srank correlations. B The SANKEY diagrams show associations between 131I therapy-related microbial genera and fecal metabolites. The mostsignificant factors for the associations marked red. C Spearman correlation analyses between α diversity indices and 131I therapy-related microbial
genera and fecal metabolites. Redundancy analysis (RDA) analysis of metabolites (D) and clinical factors (E) on the Genus level, red marked factors
contributed most to sample distribution. F Spearman correlation analyses between clinical factors important in RDA and 131I therapy-related
microbial genera and fecal metabolites. Abundance of Escherichia-Shigella (G), and levels of PC(22:2(13Z,16Z)/14:0) (H), Delta-12-Prostaglandin J2(I), and Formylanthranilic acid (J) between low and high levels of interleukin 2 receptor (IL-2). K Abundance of Escherichia-Shigella between low
and high levels of interleukin 6 (IL-6). * p < 0.05, ** p < 0.01, *** p < 0.001

and clinical factors (Fig. 6E) on the bacteria sample
distribution. The results indicated that metabolites
from the LA metabolism pathway, tumor necrosis
factor-alpha (TNFα), red blood cells (RBCs), andplatelets (PLTs) contributed the most to the sample
distribution. Moreover, these clinical factors (Fig. 6F)
and other inflammation indicators (Fig. 6G–K) showeda high correlation with the identified bacteria andmetabolites.

Predictive models for response to 131I therapy basedon microbiome and metabolism

Classifiers of logistic regression and random forest(RF) were constructed to determine the non-invasive
diagnostic markers based on gut microbial and metabolomic factors. The exploratory cohort (70%) was randomly generated from the two compared groups inthe corresponding model (Fig. 7A). Model1 consistedof 7–8 factors in two classifiers and was constructedLu et al. BMC Medicine(2024) 22:300

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Fig. 7 Predictive models combined with microbiome and metabolism for response to 131I therapy. A Schematic of sample distribution
to exploratory cohort and validation cohort. B Eight factors were selected by the forward stepwise logistic regression in discriminations
between Post- and Pre-131I-1st groups (Model1). C The importance ranking of the top seven factors in random forest model (RF) of Model1. Receiver
operating characteristic (ROC) curves in exploration set (D) and validation set (E) of Model1. F Two factors were selected by the forward stepwise
logistic regression in discriminations between Pre-131I-2nd and Pre-131I-1st groups (Model2). G The importance ranking of the top two factors in RF
of Model2. ROC curves in exploration set (H) and validation set (I) of Model2. J Two factors were selected by the forward stepwise logistic regression
in discriminations between high (≥ 150 mCi) and low (< 150 mCi) dose of 131I therapy groups (Model3). K The importance ranking of the top threefactors in RF of Model3. ROC curves in exploration set (L) and validation set (M) of Model3. DTC, differentiated thyroid cancer; HR, hazard ratio;
Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group,
patients with DTC before the second 131I therapy

to distinguish the Post-131I-1st from the Pre-131I-1stgroup (Fig. 7B, C; Additional file 5: Fig.S5A), achievinga total area under the curve (AUC) of 0.96 (logistic)

and 0.92 (RF) (Fig. 7D); this model was further validated (Z > 0.05, Fig. 7E). Over half of the common factors were found between two classifiers in Model1,

Lu et al. BMC Medicine(2024) 22:300

including PD_whole_tree in α diversity, Akkermansia,
Lachnospiraceae_UCG004, and 5,6-DHET (Fig. 7B,
C). These results indicated significant alterations in
the gut microenvironment after 131I-1st. In contrast,
Model2 consisted of PC(22:2(13Z,16Z)/14:0) anddelta-12-Prostaglandin J2 in logistic and RF classifiers,
and was constructed to discriminate between the Pre131
I-2nd and Pre-131I-1st groups (Fig. 7F, G; Additional
file 6: Fig.S5B). This model achieved a total AUC of
0.75 in the two classifiers (Fig. 7H) and showed good
validation (Z > 0.05, Fig. 7I). Despite the small difference in clinical data between the two groups (Table 1),
the changes in fecal metabolites in DTC patients
undergoing 131I therapy showed larger changes. Furthermore, Model3 consisted of 2–3 factors in logistic
and RF classifiers and was constructed to discriminate the 131I-high dose group from the 131I-low dosegroup (Fig. 7J, K; Additional file 5: Fig.S5C); the model
achieved a total AUC of 0.72 in two classifiers (Fig. 7I)
and showed good validation (Z > 0.05, Fig. 7J). Escherichia-Shigella was the only common genus in the twoclassifiers, which was sensitive to 131I therapy, consistent with the results in Fig. 3F. Besides, Model4 wasaimed to distinguish between the Post-131I-2nd groupand the Pre-131I-2nd group but could not be constructed as no factors were incorporated into models,
indicating no significant differences between the twogroups.

Page 11 of 15

Discussion
Recent research has revealed that gut microbiomes andmetabolism are significantly altered by IR [5–10, 24–26].
However, few studies have focused on the effects of IR
from radionuclides [4, 21, 27, 28]. According to a recent
position article by the European Association of Nuclear
Medicine [29], radiotherapy cannot be extrapolated tonuclear medicine directly, due to differences in dose-rate
effects, linear energy transfer, dosimetry, fractionation,
duration of treatment delivery, range, and target volume[30–32]. Therefore, an integrated study of fecal microbe
genomics and fecal metabolomes was performed in a
DTC cohort of multiple 131I therapies. The fluctuationsof intestinal flora and fecal metabolites were investigatedin the 131I treatment process, which might reflect the disrupted enteric microecology by radionuclides (Fig. 8).
The results indicated that some gut bacteria and fecal
metabolites might serve as noninvasive biomarkers for
acute and chronic responses of DTC patients under 131I
treatment.
In the early response for 131I therapy (4 days), the findings suggested a decreased abundance of Akkermansia
and Escherichia-Shigella and an increased abundanceof Bifidobacterium, Lachnoclostridium, uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_
UCG004. In the mouse model of radiation enteritis
with 18 Gy irradiation, the richness of Akkermansia andEscherichia-Shigella after irradiation might contribute to

Fig. 8 Putative mechanism of disrupted gut microbiome accompanied with fecal metabolites in DTC patients undertaking twice 131I therapy.
Fluctuations of intestinal flora and fecal radiation-sensitive metabolites in the 131I treatment progress, which might reflect and regulate entericmicroecology disrupted by radionuclide interruption

Lu et al. BMC Medicine(2024) 22:300

radiation-induced pathogenesis [33, 34]. Regarding theability to produce short-chain fatty acids (SCFA), Bifidobacterium and Lachnospiraceae are commonly recognized to play a beneficial role in gut health, further
adopted in the treatment of gastrointestinal diseases
induced by radiation [6, 35]. Our results indicated that131
I therapy (about 100 mCi) cleared harmful bacteria
and preserved the beneficial bacteria in a short period oftime. Additionally, our study identified the downregulated pathways of LA, ARA, and tryptophan metabolism
after 131I therapy. Some metabolites involved in pathwaysof tryptophan have been found to significantly decrease
the damage of radiation, such as 3-propionic acid [9],indole-3-carboxaldehyde [6], and kynurenic acid [6].
Moreover, increased production of LA, the precursor of
ARA, strongly contributed to radiation resistance, with
ARA-derived metabolite inhibitors exerting sensitizing effects, including aspirin [36, 37]. Wang’s researchand our results further verified the radioprotective effectof supplementing ARA and LA [20, 21]. Based on thesefindings, all differential pathways in our study werehypothesized to be involved in radiation resistance, andinhibiting these radiation-sensitive metabolic pathways
might result in radiation damage.
In the long-term response for 131I therapy (6 months),
strikingly different results were found compared with theearly response, including the deterioration of microbiome α diversity, with four beneficial bacteria reduced andtwo harmful bacteria improved; differentially expressed
metabolites were downregulated and radiation-sensitive
metabolic pathways were silenced; TC showed greater
progression in the Pre-131I-2nd group compared to thePre-131I-1st group, which was consistent with Zheng’s
findings [4]. Gut microbiota might be a short-term protective factor against radionuclide toxicity due to theirability to self-regulate but cannot compensate overextended periods, leading to persistent metabolic damage. 131I therapy exerts a significant negative influenceon gut microbiota and metabolism [4]. However, thealterations in bacteria or metabolites after the second131
I therapy with a higher dose were not as great as thefirst therapy, despite showing the same tendency. This
indicated the limited ability of the body to regulate gut
microbiota and metabolism against multiple radionuclide
interventions. Gambale’s study also revealed that only aminority of cases benefit from the second 131I treatment[38]. Considering the non-significant response of intestinal ecology to the second 131I treatment, additional
research should investigate the relations between thetherapeutic effect of a second 131I and the regulation ofgut microbiota and metabolism.
The integrated network analysis based on Spearman’s correlation revealed mutual interactions among

Page 12 of 15

the genera from two patterns and metabolic products
from three radiation-sensitive metabolic pathways. In
parallel, Lachnoclostridium, Lachnospiraceae_UCG004,
Escherichia-Shigella, and LA-derived metabolites were
the primary contributors to our integrated network, withhigh connection with inflammation factors across the
131
I therapy. Escherichia-Shigella was reported to play a
crucial role in proinflammatory responses [39]. Lachnospiraceae is a well-known SCFA producer, recognized asa regulator for intestinal inflammation [40, 41]. Metabolites in the LA metabolism pathway were found to haveanti-inflammatory, anti-proliferative, and antithrombotic
properties [22, 42]. Considering the proinflammatory andanti-inflammatory effects involved in these characterizedbacteria and related metabolic pathways, and the potential function of LA pathway enrichment in repairing andpromoting intestine recovery following 131I therapy [28],the replenishment of essential bacteria or metabolites
were speculated to protect against radiation toxicity via
inflammation resistance, needed to be investigated infuture studies.
Finally, three diagnostic models were established,including key microbiota and metabolites for indicating the early (Model1), long-term (Model2), and dosedependent (Model3) responses for 131I therapy by twoclassifiers. The reliability and accuracy of the three models were proved by the similar factors involved in eachmodel by different classifiers and by their corresponding
validation cohort. Model1 contains more than one-thirdof the factors of characterized bacteria and metabolites
and showed a high AUC value, indicating the great discriminative ability of the model and the significant alterations in intestinal ecology in the early response to 131I
therapy. Fewer factors were included in both Model2 andModel3 compared to Model1, which might be attributed
to the lower number of patients in the analyzed cohorts.
Nevertheless, the metabolites in the LA and ARA pathways were identified (PC(22:2(13Z,16Z)/14:0) and delta12-Prostaglandin J2) as biomarkers for distinguishing thechronic response of 131I therapy, as opposed to the minor
differences of clinical factors between DTC and Pre-131I2nd groups. As the only common bacteria observed inModel3, Escherichia-Shigellas showed strong radiosensitivity under 131I therapy and might be recognized as anindependent risk factor for screening the effective treatment dose with radionuclides in future studies.
Nevertheless, the limitations of the current study
should be acknowledged. First, despite the integrated
analyses of microbiomics and metabolomics, our study
lacks novelty as the role of gut microbiota in response
to 131I treatment in DTC patients has already been
investigated. Second, only 16S rRNA gene sequencing
and fecal metabolomic analyses were performed, andLu et al. BMC Medicine(2024) 22:300

metagenomics and serum metabolomics are necessary
for further investigation and functional analysis. Finally,
the relationship between metabolites in radiation-sensitive metabolic pathways and other microbiota under131
I therapy remains unclear. Additional experimental
verification might be required to further corroborate ourfindings.

Conclusions
Considerable changes in the gut microbes were identified with metabolic alterations in LA, ARA, and tryptophan metabolism in DTC patients during multiple 131I
therapy courses. Additionally, the cross-interactions
among characterized bacteria and metabolites from threeradiation-sensitive metabolic pathways demonstrated aclose association with different stages of 131I treatment.
Moreover, the early and long-term 131I treatment-relatedchanges in gut microbiota combined with fecal metabolites might represent a noninvasive diagnostic method forradionuclide damage.
Abbreviations
131 stI-1 	The first 131I therapy
131 ndI-2 	The second 131I therapy
ARA​	Arachidonic acid
AUC​	Area under the curve
BMI	Body mass index
CRP	C-reactive protein
DBI	Direct bilirubin
DTC	Differentiated thyroid cancer
F/B	
Firmicutes/Bacteroidetes
FDR	False discovery rate
Gran	Neutrophil
Hb	Hemoglobin
HDL	High-density lipoprotein
IL-1	Interleukin 1
IL-2	Interleukin 2 receptor
IL-6	Interleukin 6
IR	Ionizing radiation
KEGG	Kyoto Encyclopedia of Genes and Genomes
LA	Linoleic acid
LDL	Low-density lipoprotein
LEfSe	Linear discriminate analysis effect size
Lymph	Lymphocyte
OTU	Operational taxonomic unit
PCA	Principal components analysis
PCoA	Principal coordinates analysis
PLS-DA	Partial least square regressions-discriminant analysis
PLT	Platelet
Post	-131I-1stAfter the first 131I therapy
Post-131I-1st/2nd	After the second 131I therapy
Pre-131I-1st	Before the first 131I therapy
Pre-131I-2nd	Before the second 131I therapy
RBC	Red blood cell
RDA	Redundancy analysis
RF	Random forest
ROC	Receiver operating characteristic
SCFA	Short-chain fatty acids
TBI	Total bilirubinTC	Thyroid cancerTCh	Total cholesterol
Tg	Thyroglobulin
TG	Triglyceride
TgAb	Thyroglobulin antibodies

Page 13 of 15

TNFα	Tumor necrosis factor alphaTSH	ThyrotropinWBC	White blood cell

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03528-3.
Additional file 1: Fig.S1 Features of gut bacterial community compositionand structure. Α diversity analysis in Simpson (A) and Shannon (B) indicesamong four groups. (C) Ratio of Firmicutes/Bacteroidetes (F/B) among fourgroups. * p < 0.05. DTC, differentiated thyroid cancer; Pre-131I-1st group,
patients with DTC before the first 131I therapy; Post-131I-1st group, patientswith DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC
before the second 131I therapy; Post-131I-2nd group, patients with DTC
after the second 131I therapy.
Additional file 2: Fig.S2 Alterations in microbial genus compositions at
different stages of DTC with 131I therapy. Wilcoxon rank-sum test between
Post- and Pre-131I-1st (A), Pre-131I-2nd and Pre-131I-1st (B), Post- andPre-131I-2nd (C) groups. (D) Abundance of g_Akkermansia between low(< 150 mCi) and high (≥ 150 mCi) dose ­of131I therapy. * p < 0.05. DTC,
differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before
the first 131I therapy; Post-131I-1st group, patients with DTC after the first131
I therapy; Pre-131I-2nd group, patients with DTC before the second131
I therapy; Post-131I-2nd group, patients with DTC after the second 131I
therapy.
Additional file 3: Fig.S3 Linoleic acid as the potential radioprotectants
under 131I therapy. (A) Schematic of linoleic acid (LA) treatment under
2 mCi 131I therapy. (B) Spleens stained with hematoxylin and eosin (H&E)(× 200 magnification). (C) The small intestines stained with H&E (× 200
magnification, broken intestinal epithelium, black arrow), (D) alcian blue/
periodic acid-schiff (AB-PAS) (× 200 magnification, goblet cells, blackarrow) and (E, F) immunohistochemistry (IHC) (× 200 magnification,
stained with antibodies, black arrow). WP, white pulp.
Additional file 4: Supplemental Methods. Methods for the supplementation of linoleic acid in mice undergoing 131I therapy.
Additional file 5: Fig.S4 Disrupted gut microbiota interactions with fecal
metabolism and clinical factors associated with DTC patients under 131I
therapy. Spearman correlation analyses among six charactered genera(A) and 11 radiation-sensitive metabolites(B). (C) Spearman correlation
analyses between six charactered genera and 11 radiation-sensitive
metabolites.* p < 0.05,** p < 0.01,*** p < 0.001.
Additional file 6: Fig.S5 Predictive models combined with microbiome andmetabolism for response to 131I therapy. The cross-validation process was
repeated five times in random forest model of Model1 (A), Model2 (B) andModel3 (C).
Acknowledgements
We thank Home for Researchers editorial team (www.​home-​for-​resea​rchers.​com) for language editing service and Shanghai Biotree Biomedical Technology CO., LTD for sequencing service.
Authors’ contributions
All authors participated in the design of the study and/or patient enrolment
and met the criteria for authorship. Z.L., W.J., and X.Y. contributed to the study
design, study conduct and supervision, scientific overview, data analysis, andmanuscript editing. G.L. and D.G. were directly involved in the clinical diagnosis of the patients, collected and analyzed the clinical data of the subjects, analyzed the microbial and LM-MS data, and drafted the original manuscript. Y.L.
was involved in the revision of the manuscript and contributed to responding
to the reviewers’ letters. The manuscript has been substantially revised by Z.L.,
W.J., and X.Y.. G.L., D.G., and Y.L. contributed equally to this work and sharedfirst co-authorship. All authors read and approved the final manuscript.

Lu et al. BMC Medicine(2024) 22:300

Funding
This work was supported by the Science and Technology Commission of
Shanghai Municipality [grant number 22YF1433600], National Natural Science
Foundation of China [grant number: 82071964], Shanghai Leading Talent
Program sponsored by Shanghai Human Resources and Social Security Bureau[grant number: 2019], Shanghai Municipal Health Commission [grant number:
GWVI-11.2-YQ51].
Availability of data and materials
The original data presented in this study are available from the corresponding
author upon reasonable request.

Declarations
Ethics approval and consent to participate
The study was carried out following the Declaration of Helsinki principles andapproved by the Ethics Committee of the Shanghai Tenth People’s Hospital(No. SHSY-IEC-5.0/22K13/P01). Informed, written consent was obtained fromall patients.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Clinical Nuclear Medicine Center, Imaging Clinical Medical Center, Instituteof Nuclear Medicine, Institute of Clinical Mass Spectrometry Applied ResearchCenter, Department of Nuclear Medicine, Shanghai Tenth People’s Hospital,
School of Medicine, Tongji University, Shanghai 200072, China. 2 Department of Gynecology and Obstetrics, Shanghai Tenth People’s Hospital,
School of Medicine, Tongji University, Shanghai 200072, China. 3 Departmentof Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji
University, Shanghai 200092, China. 4 Shanghai Public Health Clinical Center,
Fudan University, Shanghai 200003, China.
Received: 6 February 2024 Accepted: 10 July 2024

References
1. Kitahara CM, Sosa JA. The changing incidence of thyroid cancer. Nat Rev
Endocrinol. 2016;12(11):646–53. https://​doi.​org/​10.​1038/​nrendo.​2016.​110.
2. Hurst Z, Liyanarachchi S, He H, Brock P, Sipos J, Nabhan F, et al. Risk Haplotypes Uniquely Associated with Radioiodine-Refractory Thyroid Cancer
Patients of High African Ancestry. Thyroid. 2019;29(4):530–9. https://​doi.​
org/​10.​1089/​thy.​2018.​0687.
3. Fard-Esfahani A, Emami-Ardekani A, Fallahi B, Fard-Esfahani P, Beiki D,
Hassanzadeh-Rad A, et al. Adverse effects of radioactive iodine-131
treatment for differentiated thyroid carcinoma. Nucl Med Commun.
2014;35(8):808–17. https://​doi.​org/​10.​1097/​mnm.​00000​00000​000132.
4. Zheng L, Zhang L, Tang L, Huang D, Pan D, Guo W, et al. Gut microbiota is
associated with response to (131)I therapy in patients with papillary thyroid carcinoma. Eur J Nucl Med Mol Imaging. 2023;50(5):1453–65. https://​doi.​org/​10.​1007/​s00259-​022-​06072-5.
5. Yu Y, Lin X, Feng F, Wei Y, Wei S, Gong Y, et al. Gut microbiota and ionizing
radiation-induced damage: Is there a link? Environ Res. 2023;229:115947.
https://​doi.​org/​10.​1016/j.​envres.​2023.​115947.
6. Guo H, Chou WC, Lai Y, Liang K, Tam JW, Brickey WJ, et al. Multi-omics
analyses of radiation survivors identify radioprotective microbes andmetabolites. Science. 2020;370 6516 https://​doi.​org/​10.​1126/​scien​ce.​aay90​97.
7. Lavrinienko A, Mappes T, Tukalenko E, Mousseau TA, Møller AP, Knight R,
et al. Environmental radiation alters the gut microbiome of the bank vole
Myodes glareolus. Isme j. 2018;12(11):2801–6. https://​doi.​org/​10.​1038/​
s41396-​018-​0214-x.

Page 14 of 15

8.

Kalkeri R, Walters K, Van Der Pol W, McFarland BC, Fisher N, Koide F, et al.
Changes in the gut microbiome community of nonhuman primatesfollowing radiation injury. BMC Microbiol. 2021;21(1):93. https://​doi.​
org/​10.​1186/​s12866-​021-​02146-w.
9. Xiao HW, Cui M, Li Y, Dong JL, Zhang SQ, Zhu CC, et al. Gut microbiotaderived indole 3-propionic acid protects against radiation toxicity via
retaining acyl-CoA-binding protein. Microbiome. 2020;8(1):69. https://​doi.​org/​10.​1186/​s40168-​020-​00845-6.
10. Cui M, Xiao H, Li Y, Zhou L, Zhao S, Luo D, et al. Faecal microbiota transplantation protects against radiation-induced toxicity. EMBO Mol Med.
2017;9(4):448–61. https://​doi.​org/​10.​15252/​emmm.​20160​6932.
11. Lu G, Yu X, Jiang W, Luo Q, Tong J, Fan S, et al. Alterations of Gut
Microbiome and Metabolite Profiles Associated With Anabatic Lipid
Dysmetabolism in Thyroid Cancer. Front Endocrinol (Lausanne).
2022;13:893164. https://​doi.​org/​10.​3389/​fendo.​2022.​893164.
12. Yu X, Jiang W, Kosik RO, Song Y, Luo Q, Qiao T, et al. Gut microbiotachanges and its potential relations with thyroid carcinoma. J Adv Res.
2022;35:61–70. https://​doi.​org/​10.​1016/j.​jare.​2021.​04.​001.
13. Schlumberger M, Leboulleux S. Current practice in patients with differentiated thyroid cancer. Nat Rev Endocrinol. 2021;17(3):176–88. https://​doi.​org/​10.​1038/​s41574-​020-​00448-z.
14. Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J.
Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut.
2016;65(1):57–62. https://​doi.​org/​10.​1136/​gutjnl-​2015-​309618.
15. Chen K, Luan X, Liu Q, Wang J, Chang X, Snijders AM, et al. Drosophila
Histone Demethylase KDM5 Regulates Social Behavior through
Immune Control and Gut Microbiota Maintenance. Cell Host Microbe.
2019;25(4):537-52.e8. https://​doi.​org/​10.​1016/j.​chom.​2019.​02.​003.
16. Yang C, Xu Z, Deng Q, Huang Q, Wang X, Huang F. Beneficial effects offlaxseed polysaccharides on metabolic syndrome via gut microbiota in
high-fat diet fed mice. Food Res Int. 2020;131:108994. https://​doi.​org/​
10.​1016/j.​foodr​es.​2020.​108994.
17. Sheng D, Zhao S, Gao L, Zheng H, Liu W, Hou J, et al. BabaoDan attenuates high-fat diet-induced non-alcoholic fatty liver disease via activation of AMPK signaling. Cell Biosci. 2019;9:77. https://​doi.​org/​10.​1186/​
s13578-​019-​0339-2.
18. Song HJ, Qiu ZL, Shen CT, Wei WJ, Luo QY. Pulmonary metastases
in differentiated thyroid cancer: efficacy of radioiodine therapy andprognostic factors. Eur J Endocrinol. 2015;173(3):399–408. https://​doi.​
org/​10.​1530/​eje-​15-​0296.
19. Ciarallo A, Rivera J. Radioactive Iodine Therapy in Differentiated Thyroid
Cancer: 2020 Update. AJR Am J Roentgenol. 2020;215(2):285–91.
https://​doi.​org/​10.​2214/​ajr.​19.​22626.
20. Wang Q, Lin Y, Sheng X, Xu J, Hou X, Li Y, et al. Arachidonic Acid Promotes Intestinal Regeneration by Activating WNT Signaling. Stem Cell
Reports. 2020;15(2):374–88. https://​doi.​org/​10.​1016/j.​stemcr.​2020.​06.​009.
21. Lu G, Gao D, Jiang W, Yu X, Tong J, Liu X, et al. Disrupted gut microecology after high-dose (131)I therapy and radioprotective effects ofarachidonic acid supplementation. Eur J Nucl Med Mol Imaging. 2024.
https://​doi.​org/​10.​1007/​s00259-​024-​06688-9.
22. Zhao J, Cheng W, Lu H, Shan A, Zhang Q, Sun X, et al. High fiber dietattenuate the inflammation and adverse remodeling of myocardial
infarction via modulation of gut microbiota and metabolites. Front
Microbiol. 2022;13:1046912. https://​doi.​org/​10.​3389/​fmicb.​2022.​10469​
12.
23. Alarcon-Gil J, Sierra-Magro A, Morales-Garcia JA, Sanz-SanCristobal M,
Alonso-Gil S, Cortes-Canteli M, et al. Neuroprotective and Anti-Inflammatory Effects of Linoleic Acid in Models of Parkinson’s Disease: The Implication of Lipid Droplets and Lipophagy. Cells. 2022;11 15 https://​doi.​org/​10.​3390/​cells​11152​297.
24. Manichanh C, Varela E, Martinez C, Antolin M, Llopis M, Doré J, et al. The
gut microbiota predispose to the pathophysiology of acute postradiotherapy diarrhea. Am J Gastroenterol. 2008;103(7):1754–61. https://​doi.​
org/​10.​1111/j.​1572-​0241.​2008.​01868.x.
25. Yi Y, Shen L, Shi W, Xia F, Zhang H, Wang Y, et al. Gut Microbiome Components Predict Response to Neoadjuvant Chemoradiotherapy in Patientswith Locally Advanced Rectal Cancer: A Prospective. Longitudinal Study
Clin Cancer Res. 2021;27(5):1329–40. https://​doi.​org/​10.​1158/​1078-​0432.​
Ccr-​20-​3445.

Lu et al. BMC Medicine(2024) 22:300

26. Kumagai T, Rahman F and Smith AM. The Microbiome and Radiation
Induced-Bowel Injury: Evidence for Potential Mechanistic Role in Disease
Pathogenesis. Nutrients. 2018;10:10. https://​doi.​org/​10.​3390/​nu101​01405.
27. Fernandes A, Oliveira A, Guedes C, Fernandes R, Soares R and Barata
P. Ionizing Radiation from Radiopharmaceuticals and the Human Gut
Microbiota: An Ex Vivo Approach. Int J Mol Sci. 2022;23:18. https://​doi.​
org/​10.​3390/​ijms2​31810​809.
28. Li W, Cheng F, Zhang J, Li C, Yu D, Simayijiang H, et al. Changes in Gut
Microbiota and Metabolites in Papillary Thyroid Carcinoma Patients
Following Radioactive Iodine Therapy. Int J Gen Med. 2023;16:4453–64.
https://​doi.​org/​10.​2147/​ijgm.​S4334​33.
29. Aerts A, Eberlein U, Holm S, Hustinx R, Konijnenberg M, Strigari L, et al.
EANM position paper on the role of radiobiology in nuclear medicine. Eur
J Nucl Med Mol Imaging. 2021;48(11):3365–77. https://​doi.​org/​10.​1007/​
s00259-​021-​05345-9.
30. Terry SYA, Nonnekens J, Aerts A, Baatout S, de Jong M, Cornelissen B, et al.
Call to arms: need for radiobiology in molecular radionuclide therapy. Eur
J Nucl Med Mol Imaging. 2019;46(8):1588–90. https://​doi.​org/​10.​1007/​
s00259-​019-​04334-3.
31. Morris ZS, Wang AZ, Knox SJ. The Radiobiology of Radiopharmaceuticals.
Semin Radiat Oncol. 2021;31(1):20–7. https://​doi.​org/​10.​1016/j.​semra​donc.​2020.​07.​002.
32. Pouget JP, Lozza C, Deshayes E, Boudousq V, Navarro-Teulon I. Introduction to radiobiology of targeted radionuclide therapy. Front Med(Lausanne). 2015;2:12. https://​doi.​org/​10.​3389/​fmed.​2015.​00012.
33. Li Y, Yan H, Zhang Y, Li Q, Yu L, Li Q, et al. Alterations of the Gut Microbiome Composition and Lipid Metabolic Profile in Radiation Enteritis. Front
Cell Infect Microbiol. 2020;10:541178. https://​doi.​org/​10.​3389/​fcimb.​2020.​
541178.
34. Shakyawar SK, Mishra NK, Vellichirammal NN, Cary L, Helikar T, Powers R, et al. A Review of Radiation-Induced Alterations of Multi-Omic
Profiles, Radiation Injury Biomarkers, and Countermeasures. Radiat Res.
2023;199(1):89–111. https://​doi.​org/​10.​1667/​rade-​21-​00187.1.
35. Yamanouchi K, Tsujiguchi T, Sakamoto Y, Ito K. Short-term follow-up ofintestinal flora in radiation-exposed mice. J Radiat Res. 2019;60(3):328–32.
https://​doi.​org/​10.​1093/​jrr/​rrz002.
36. Lewis JE, Kemp ML. Integration of machine learning and genome-scale
metabolic modeling identifies multi-omics biomarkers for radiation
resistance. Nat Commun. 2021;12(1):2700. https://​doi.​org/​10.​1038/​
s41467-​021-​22989-1.
37. Choy H, Milas L. Enhancing radiotherapy with cyclooxygenase-2 enzyme
inhibitors: a rational advance? J Natl Cancer Inst. 2003;95(19):1440–52.
https://​doi.​org/​10.​1093/​jnci/​djg058.
38. Gambale C, Prete A, Contartese L, Torregrossa L, Bianchi F, Molinaro E,
et al. Usefulness of second 131I treatment in biochemical persistentdifferentiated thyroid cancer patients. Eur Thyroid J. 2023;12 6 https://​doi.​
org/​10.​1530/​etj-​23-​0052.
39. Xie J, Liu Y, Chen B, Zhang G, Ou S, Luo J, et al. Ganoderma lucidum polysaccharide improves rat DSS-induced colitis by altering cecal microbiota
and gene expression of colonic epithelial cells. Food Nutr Res. 2019;63
https://​doi.​org/​10.​29219/​fnr.​v63.​1559.
40. Borton MA, Sabag-Daigle A, Wu J, Solden LM, O’Banion BS, Daly RA,
et al. Chemical and pathogen-induced inflammation disrupt the murine
intestinal microbiome. Microbiome. 2017;5(1):47. https://​doi.​org/​10.​1186/​
s40168-​017-​0264-8.
41. Vacca M, Celano G, Calabrese FM, Portincasa P, Gobbetti M and De Angelis M. The Controversial Role of Human Gut Lachnospiraceae. Microorganisms. 2020;8 4 https://​doi.​org/​10.​3390/​micro​organ​isms8​040573.
42. Jang YO, Kim OH, Kim SJ, Lee SH, Yun S, Lim SE, et al. High-fiber diets
attenuate emphysema development via modulation of gut microbiota
and metabolism. Sci Rep. 2021;11(1):7008. https://​doi.​org/​10.​1038/​
s41598-​021-​86404-x.

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